• 1 Introduction
• 2 Clinical
• 3 Samples sequending statistics
• 4 Vsisualization

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1 Introduction

This is introduction

Requires

library(tidyverse)<br>
library(ggthemes)<br>
library(ggsci)<br>
library(ggpubr)<br>
library(survminer)<br>
library(survival)<br>
library(survivalROC)<br>
library(reshape2)<br>
library(data.table)<br>
library(ggExtra)<br>
library(cowplot)<br>
library(ComplexHeatmap)<br>
library(scico)<br>
library(colorspace)<br>
library(RColorBrewer)<br>
library(lubridate)<br>
library(tableone)<br>
library(kableExtra)<br>
source("../R_function/colors.R")<br>
source("../R_function/surv_plot.R")<br>
theme_set(theme_cowplot())<br>
"%ni%" <- Negate("%in%")<br>
options(stringsAsFactors = F)<br>

2 Clinical

Clinical.csv

cli<-fread("../Data/Data/clinical.csv",data.table = F)
factorvars <- colnames(cli)[-c(1:3,22:25)]
tableone_groups <- CreateTableOne(vars = colnames(cli)[-c(1:3,22:25)],
                                  strata = 'Response',
                                  data = cli, 
                                  factorVars = factorvars)
table1_groups <- print(x = tableone_groups, 
                       contDigits = 1,     
                       exact = factorvars, 
                       showAllLevels = FALSE, 
                       noSpaces = TRUE, 
                       printToggle = FALSE) 
table1_groups %>%
  knitr::kable(caption = "Recreating booktabs style table") 

Recreating booktabs style table

	NE	NR	R	p	test
n	6	21	13
HandFoodSyndrom (%)				0.347	exact
Level0	3 (50.0)	12 (57.1)	3 (23.1)
Level1	1 (16.7)	3 (14.3)	2 (15.4)
Level2	1 (16.7)	5 (23.8)	4 (30.8)
Level3	1 (16.7)	1 (4.8)	4 (30.8)
Rash (%)				0.836	exact
Level0	5 (83.3)	13 (61.9)	9 (69.2)
Level1	0 (0.0)	4 (19.0)	3 (23.1)
Level2	1 (16.7)	2 (9.5)	1 (7.7)
Level3	0 (0.0)	2 (9.5)	0 (0.0)
Fever (%)				0.481	exact
Level0	5 (83.3)	18 (85.7)	9 (69.2)
Level1	1 (16.7)	1 (4.8)	1 (7.7)
Level2	0 (0.0)	2 (9.5)	3 (23.1)
Diarrhea (%)				0.957	exact
Level0	5 (83.3)	16 (76.2)	12 (92.3)
Level1	1 (16.7)	3 (14.3)	1 (7.7)
Level2	0 (0.0)	1 (4.8)	0 (0.0)
Level3	0 (0.0)	1 (4.8)	0 (0.0)
Gender = Male (%)	2 (33.3)	12 (57.1)	7 (53.8)	0.623	exact
Age = >60 (%)	0 (0.0)	6 (28.6)	3 (23.1)	0.505	exact
BMI = >=25 (%)	2 (33.3)	7 (33.3)	2 (15.4)	0.550	exact
History = yes (%)	1 (16.7)	6 (28.6)	4 (30.8)	1.000	exact
Smoking = yes (%)	2 (33.3)	2 (9.5)	2 (15.4)	0.265	exact
Dringking = yes (%)	2 (33.3)	1 (4.8)	2 (15.4)	0.154	exact
anitEGFR = yes (%)	2 (33.3)	5 (23.8)	5 (38.5)	0.642	exact
antiVEGF = yes (%)	3 (50.0)	13 (61.9)	7 (53.8)	0.829	exact
LiverM = yes (%)	5 (83.3)	14 (66.7)	9 (69.2)	0.896	exact
LungM = yes (%)	5 (83.3)	10 (47.6)	9 (69.2)	0.248	exact
LymphM = yes (%)	2 (33.3)	10 (47.6)	6 (46.2)	0.911	exact
PeritonealM = yes (%)	0 (0.0)	8 (38.1)	2 (15.4)	0.142	exact
OtherM = yes (%)	1 (16.7)	5 (23.8)	0 (0.0)	0.162	exact
Location = right (%)	3 (50.0)	6 (28.6)	4 (30.8)	0.590	exact

3 Samples sequending statistics

data<-fread("../Data/Data/samples_seqInfo.csv",data.table = F)
knitr::include_graphics("../images/samples_seqInfo.png")

4 Vsisualization

df<-fread("../Data/Data/Phylum_cli_111samples.csv",data.table = F)
df$FBratio<-df$Firmicutes/df$Bacteroidetes
df$FBratio_g<-ifelse(df$FBratio>=median(df$FBratio),"High","Low")
data<-subset(df,Site=="Stool"&Response!="NE"&Cycle=="BL")
par(mfrow=c(1,2))
hist(data$BMI,main="Frequence of BMI",xlab = "BMI")
hist(data$FBratio,main="Frequence of FBratio",xlab = "FBratio")

data$BMI_g<-ifelse(data$BMI>25,"High","Low")
data$FBratio_g<-ifelse(data$FBratio>median(data$FBratio),"High","Low")
fit<-survfit(Surv(PFStime,PFS) ~ BMI_g,
                   data = data)
fit

## Call: survfit(formula = Surv(PFStime, PFS) ~ BMI_g, data = data)
## 
##             n events median 0.95LCL 0.95UCL
## BMI_g=High 10      8   3.28    2.20      NA
## BMI_g=Low  22     21   1.97    1.87     4.2

ggsurvplot(fit, data=data,xlab = "Time(months)",
           censor.size=0.5, size = 0.5,
           conf.int = T,tables.theme = theme_few(base_size = 6),
           linetype = "strata",
                legend.title = "",palette = c("black","red"),
                risk.table = T,
                #legend = c(0.84, 0.8),
                pval = TRUE,pval.size = 3, 
                pval.coord=c(0.8,0.2),pval.method=F,
                pval.method.coord=c(0.05,0.3), 
                ggtheme = theme_minimal() + 
                  theme(line = element_line(size = 0.1),
                        text  = element_text(size = 6)),
                risk.table.col = "strata",
                surv.median.line = "hv",
                risk.table.y.text.col = T,
                risk.table.y.text = FALSE )

p1<-ggscatter(subset(df,Cycle=="BL"&Response!="NE"&FBratio<10), x = "FBratio", y = "BMI",size=0.5,mean.point = T,
          color = "Site", add.params = list(c(size=0.5,color="Site")),
          add = "reg.line", conf.int = TRUE)+
  stat_cor(label.x = 0.3,aes(color=Site))+
  theme_few(base_size = 8)+
  scale_color_aaas()+
  scale_color_manual(values = col11)

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

p2<-ggscatter(subset(df,Cycle=="BL"&Response!="NE"), x = "Firmicutes", y = "Bacteroidetes",size=0.5,mean.point = T,
          color = "Site", add.params = list(c(size=0.5,color="Site")),
          add = "reg.line", conf.int = TRUE)+
  stat_cor(label.x = 0.2,aes(color=Site))+
  theme_few(base_size = 8)+
  scale_color_aaas()

p3<-ggstatsplot::ggbarstats(data = data,x=Response,ggtheme = ggplot2::theme_bw(base_size=8),bias.correct = T,
                        y =FBratio_g,subtitle = F,results.subtitle=F,
                        ggstatsplot.layer = FALSE,
                        legend.position="right",
                        messages = FALSE,
                        package = "ggsci",
                        palette = "default_nejm",
                        main = Response, nboot = 100,
                        legend.title = "Response")

## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car

plot_grid(p1,p2, p3,labels = c("A","B","C"), ncol =3, nrow = 1)

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using formula 'y ~ x'

df <- fread("../Data/Data/paired_BL_treat_16patients.csv", data.table = F)

df$Hand_food_syndrom <- as.factor(df$Hand_food_syndrom)
df$Hand_food_syndrom_g <- ifelse(df$Hand_food_syndrom %in% c("0", "1"), "no", "yes")
df$Rash <- as.factor(df$Rash)
df$Rash_g <- ifelse(df$Rash == "0", "no", "yes")
df$Fever <- as.factor(df$Fever)
df$Fever_g <- ifelse(df$Fever == "0", "no", "yes")
df$Diarrhea <- as.factor(df$Diarrhea)
df$Diarrhea_g <- ifelse(df$Diarrhea == "0", "no", "yes")

df_treat <- subset(df, Group == "Treat")
# List of ggsurvplots
require("survminer")
splots <- list()

fit_PFS <- survfit(Surv(PFStime, PFS) ~ Hand_food_syndrom_g, data = df_treat)
fit_PFS
fit_OS <- survfit(Surv(OStime, OS) ~ Hand_food_syndrom_g, data = df_treat)
fit_OS
splots[[1]] <- surv_plot(fit_PFS, df_treat, colors = c("darkgreen", "darkorange"), 
    title = "HandFoodSyndrom_PFS")
## Loading required package: prodlim
splots[[2]] <- surv_plot(fit_OS, df_treat, colors = c("black", "red"), title = "HandFoodSyndrom_OS")

fit_PFS <- survfit(Surv(PFStime, PFS) ~ Rash_g, data = df_treat)
fit_PFS
fit_OS <- survfit(Surv(OStime, OS) ~ Rash_g, data = df_treat)
fit_OS
splots[[3]] <- surv_plot(fit_PFS, df_treat, colors = c("darkgreen", "darkorange"), 
    title = "Rash_PFS")
splots[[4]] <- surv_plot(fit_OS, df_treat, colors = c("black", "red"), title = "Rash_OS")

fit_PFS <- survfit(Surv(PFStime, PFS) ~ Fever_g, data = df_treat)
fit_PFS
fit_OS <- survfit(Surv(OStime, OS) ~ Fever_g, data = df_treat)
fit_OS
splots[[5]] <- surv_plot(fit_PFS, df_treat, colors = c("darkgreen", "darkorange"), 
    title = "Fever_PFS")
splots[[6]] <- surv_plot(fit_OS, df_treat, colors = c("black", "red"), title = "Fever_OS")
fit_PFS <- survfit(Surv(PFStime, PFS) ~ Diarrhea_g, data = df_treat)
fit_PFS
fit_OS <- survfit(Surv(OStime, OS) ~ Diarrhea_g, data = df_treat)
fit_OS
splots[[7]] <- surv_plot(fit_PFS, df_treat, colors = c("darkgreen", "darkorange"), 
    title = "Diarrhea_PFS")
splots[[8]] <- surv_plot(fit_OS, df_treat, colors = c("black", "red"), title = "Diarrhea_OS")
arrange_ggsurvplots(splots, print = TRUE, ncol = 4, nrow = 2)

bar1 <- ggplot(df, aes(Group, Desulfovibrionaceae, fill = Response)) + geom_boxplot() + 
    geom_line(aes(group = patientID, color = Response, size = Desulfovibrionaceae), 
        alpha = 0.5) + geom_point(aes(size = Desulfovibrionaceae), color = "darkblue", 
    alpha = 0.5) + theme_few(base_size = 8) + stat_compare_means(label = "p.signif") + 
    scale_fill_d3() + theme(legend.key = element_blank(), axis.title.x = element_blank())
bar2 <- ggplot(df, aes(Group, Desulfovibrionaceae, fill = Diarrhea_g)) + geom_boxplot() + 
    geom_line(aes(group = patientID, color = Diarrhea_g, size = Desulfovibrionaceae), 
        alpha = 0.5) + geom_point(aes(size = Desulfovibrionaceae), color = "darkblue", 
    alpha = 0.5) + theme_few(base_size = 8) + stat_compare_means(label = "p.signif") + 
    scale_fill_jama() + theme(legend.key = element_blank(), axis.title.x = element_blank())
## Call: survfit(formula = Surv(PFStime, PFS) ~ Hand_food_syndrom_g, data = df_treat)
## 
##                         n events median 0.95LCL 0.95UCL
## Hand_food_syndrom_g=no  7      7    2.3    1.97      NA
## Hand_food_syndrom_g=yes 9      7    4.2    2.20      NA
## Call: survfit(formula = Surv(OStime, OS) ~ Hand_food_syndrom_g, data = df_treat)
## 
##                         n events median 0.95LCL 0.95UCL
## Hand_food_syndrom_g=no  7      2     NA    5.17      NA
## Hand_food_syndrom_g=yes 9      3   15.5   10.33      NA
## Call: survfit(formula = Surv(PFStime, PFS) ~ Rash_g, data = df_treat)
## 
##             n events median 0.95LCL 0.95UCL
## Rash_g=no  11     10   3.80    2.03      NA
## Rash_g=yes  5      4   4.23    2.20      NA
## Call: survfit(formula = Surv(OStime, OS) ~ Rash_g, data = df_treat)
## 
##             n events median 0.95LCL 0.95UCL
## Rash_g=no  11      3   10.3    10.3      NA
## Rash_g=yes  5      2   15.5    15.5      NA
## Call: survfit(formula = Surv(PFStime, PFS) ~ Fever_g, data = df_treat)
## 
##              n events median 0.95LCL 0.95UCL
## Fever_g=no  14     12   3.05    2.03      NA
## Fever_g=yes  2      2   5.28    4.23      NA
## Call: survfit(formula = Surv(OStime, OS) ~ Fever_g, data = df_treat)
## 
##              n events median 0.95LCL 0.95UCL
## Fever_g=no  14      4   15.5    15.5      NA
## Fever_g=yes  2      1   10.3      NA      NA
## Call: survfit(formula = Surv(PFStime, PFS) ~ Diarrhea_g, data = df_treat)
## 
##                 n events median 0.95LCL 0.95UCL
## Diarrhea_g=no  13     11   4.23    2.30      NA
## Diarrhea_g=yes  3      3   1.97    1.87      NA
## Call: survfit(formula = Surv(OStime, OS) ~ Diarrhea_g, data = df_treat)
## 
##                 n events median 0.95LCL 0.95UCL
## Diarrhea_g=no  13      4   15.5    10.3      NA
## Diarrhea_g=yes  3      1     NA     3.9      NA

plot_grid(bar1, bar2, labels = c("A", "B"), ncol = 2, nrow = 1)